Abstract
While kernel methods are the basis of many popular techniques in supervised learning, they are less commonly used in testing, estimation, and analysis of probability distributions, where information theoretic approaches rule the roost. However it becomes difficult to estimate mutual information or entropy if the data are high dimensional.
The full version of this paper is published in the Proceedings of the 18th International Conference on Algorithmic Learning Theory, ALT 2007, Lecture Notes in Artificial Intelligence Vol. 4754.
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© 2007 Springer-Verlag Berlin Heidelberg
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Smola, A., Gretton, A., Song, L., Schölkopf, B. (2007). A Hilbert Space Embedding for Distributions. In: Corruble, V., Takeda, M., Suzuki, E. (eds) Discovery Science. DS 2007. Lecture Notes in Computer Science(), vol 4755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75488-6_5
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DOI: https://doi.org/10.1007/978-3-540-75488-6_5
Publisher Name: Springer, Berlin, Heidelberg
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